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Cited by 24Artificial Intelligence in EducationInnovative Teaching MethodsArtificial Intelligence in Healthcare and Education

Reimagining flipped learning via bloom's taxonomy and student–teacher–GenAI interactions

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Kwan, Paul, Kadel, Rajan, Memon, Tayab D., Hashmi, Saad S.·2025·Education Sciences
DOI 10.3390/educsci15040465
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Abstract

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.

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  1. Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of bloom's taxonomy of educational objectives: Complete edition. Addison Wesley Longman, Inc.
  2. Ariza, J. Á., Restrepo, M. B., & Hernández, C. H. (2025). Generative AI in engineering and computing education: A scoping review of empirical studies and educational practices. IEEE Access, 13, 30789-30810.
  3. Barbetta, P. M. (2023). Technologies as tools to increase active learning during online higher-education instruction. Journal of Educational Technology Systems, 51(3), 317-339.
  4. Butt, A. (2014). Student views on the use of a flipped classroom approach: Evidence from Australia. Business Education & Accreditation, 6(1), 33-43.
  5. Cevikbas, M., & Kaiser, G. (2022). Promoting personalized learning in flipped classrooms: A systematic review study. Sustainability, 14(18), 11393.
  6. Chen, T., Luo, H., Wang, P., Yin, X., & Yang, J. (2023). The role of pre-class and in-class behaviors in predicting learning performance and experience in flipped classrooms. Heliyon, 9(4), e15234.
  7. Clark, R. M., Kaw, A. K., & Braga Gomes, R. (2022). Adaptive learning: Helpful to the flipped classroom in the online environment of COVID? Computer Applications in Engineering Education, 30(2), 517-531.
  8. Cueva, A., & Inga, E. (2022). Information and communication technologies for education considering the flipped learning model. Education Sciences, 12(3), 207.
  9. Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., JonesMitchell, N., Ong, D. C., Dweck, C. S., Gross, J. J., & Pennebaker, J. W. (2023). Using large language models in psychology. Nature Reviews Psychology, 2(11), 688-701.
  10. Deng, R., & Gao, Y. (2024). Effects of embedded questions in pre-class videos on learner perceptions, video engagement, and learning performance in flipped classrooms. Active Learning in Higher Education, 25(3), 14697874231167098.
  11. Dickey, E., & Bejarano, A. (2023). A model for integrating generative AI into course content development. arXiv, arXiv:2308.12276. · arXiv
  12. Gupta, N., Khatri, K., Malik, Y., Lakhani, A., Kanwal, A., Aggarwal, S., & Dahuja, A. (2024). Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training. BMC Medical Education, 24(1), 1544.
  13. Hall, A. A., & DuFrene, D. D. (2016). Best practices for launching a flipped classroom. Business and Professional Communication Quarterly, 79(2), 234-242.
  14. Hill, J., & Smith, K. (2023). Visions of blended learning: Identifying the challenges and opportunities in shaping institutional approaches to blended learning in higher education. Technology, Pedagogy and Education, 32(3), 289-303.
  15. Howell, R. A. (2021). Engaging students in education for sustainable development: The benefits of active learning, reflective practices and flipped classroom pedagogies. Journal of Cleaner Production, 325, 129318.
  16. Hung, H.-T. (2015). Flipping the classroom for English language learners to foster active learning. Computer Assisted Language Learning, 28(1), 81-96.
  17. Karabulut-Ilgu, A., Jaramillo Cherrez, N., & Jahren, C. T. (2018). A systematic review of research on the flipped learning method in engineering education. British Journal of Educational Technology, 49(3), 398-411.
  18. Kim, N. H., So, H.-J., & Joo, Y. J. (2021). Flipped learning design fidelity, self-regulated learning, satisfaction, and continuance intention in a university flipped learning course. Australasian Journal of Educational Technology, 37(4), 1-19.
  19. Kwan, P., Kadel, R., Memon, T. D., & Hashmi, S. S. (2024). Intensifying learner engagement and focus by a block mode flipped learning pedagogy. In Flipped classrooms and learning: Perspectives, opportunities and challenges (pp. 9-33). Nova Science Publishers Inc.
  20. Kwan, P., Memon, T. D., Hashmi, S. S., Rhode, F., & Kadel, R. (2022). An empirical study of students' perception of and key factors affecting overall satisfaction in an intensive block mode and flipped classroom. Education Sciences, 12(8), 535.
  21. Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. The Journal of Economic Education, 31(1), 30-43.
  22. Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174.
  23. Li, H.-F. (2023). Effects of a ChatGPT-based flipped learning guiding approach on learners' courseware project performances and perceptions. Australasian Journal of Educational Technology, 39(5), 40-58.
  24. Lo, C. K., & Hew, K. F. (2017). A critical review of flipped classroom challenges in K-12 education: Possible solutions and recommendations for future research. Research and Practice in Technology Enhanced Learning, 12, 1-22.
  25. Meli, K., Taouki, J., & Pantazatos, D. (2024, July 1-3). Empowering educators with generative AI: The genai education frontier initiative [Conference session]. EDULEARN24 16th Annual International Conference on Education and New Learning Technologies (pp. 4289-4299), Palma de Mallorca, Spain.
  26. Ngoc, T. N., Tran, Q. N., Tang, A., Nguyen, B., Nguyen, T., & Pham, T. (2023). Ai-assisted learning for electronic engineering courses in high education. arXiv, arXiv:2311.01048. · arXiv
  27. O'Flaherty, J., & Phillips, C. (2015). The use of flipped classrooms in higher education: A scoping review. The Internet and Higher Education, 25, 85-95.
  28. Phelps, C., & Moro, C. (2022). Using live interactive polling to enable hands-on learning for both face-to-face and online students within hybrid-delivered courses. Journal of University Teaching and Learning Practice, 19(3), 8.
  29. Rajaram, K. (2019). Flipped classrooms: Providing a scaffolding support system with real-time learning interventions. International Journal for the Scholarship of Teaching and Learning, 9(1), 30-58.
  30. Roach, T. (2014). Student perceptions toward flipped learning: New methods to increase interaction and active learning in economics. International Review of Economics Education, 17, 74-84.
  31. Sargent, J., & Casey, A. (2020). Flipped learning, pedagogy and digital technology: Establishing consistent practice to optimise lesson time. European Physical Education Review, 26(1), 70-84.
  32. Shailendra, S., Kadel, R., & Sharma, A. (2024). Framework for adoption of generative artificial intelligence (GenAI) in education. IEEE Transactions on Education, 67(5), 777-785.
  33. Tucker, S. Y. (2014). Transforming pedagogies: Integrating 21st century skills and Web 2.0 technology. Turkish Online Journal of Distance Education, 15(1), 166-173.
  34. Walsh, J. N., O'Brien, M. P., & Costin, Y. (2021). Investigating student engagement with intentional content: An exploratory study of instructional videos. The International Journal of Management Education, 19(2), 100505.
  35. Wong, C.-M., Zhou, L., Patel, M., Xu, M., & Tang, Y.-M. (2024, June 18-21). Harnessing the power of cutting-edge technologies in flipped learning: A paradigm shift in university education after post-COVID-19 era [Conference session]. 10th International Conference on Higher Education Advances (HEAd'24), Valencia, Spain.
  36. Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
  37. Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839-1850.
  38. Zaphir, L., Lodge, J. M., Lisec, J., McGrath, D., & Khosravi, H. (2024). How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence. arXiv, arXiv:2406.14769. · arXiv
  39. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28.

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Authors
Kwan, P., Kadel, R., Memon, T. D., & Hashmi, S. S.
Affiliations
  • Central Queensland University (AU)
  • Melbourne Institute of Technology (AU)
  • Torrens University Australia (AU)
  • Independent Researcher, Sydney, NSW 2000, Australia
Corresponding author
Kwan, Paul
ORCID
Venue
Education Sciences
Publisher
MDPI AG
Published
2025-04-08
Volume
15
Issue
4
Pages
465
ISSN
2227-7102 (ISSN-L)
DOI
10.3390/educsci15040465
OpenAlex
W4409296175
Cited by
24
References
54
Citations by year
2026: 12 · 2025: 12
FWCI
25.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Education Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Innovative Teaching Methods
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
flipped learning, generative AI, ChatGPT, Bloom's taxonomy, personalized learning, cooperative learning, real-time feedback, AI in teaching, 21st century skills, critical thinking

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